In Python, decorators allow Data Scientists to extend and modify callables, such as functions, methods and classes, without explicitly changing the callable. Using decorators can improve the readability of your code as well code flexibility and modularity. In this article, we’ll discuss why we would use decorators, how to implement decorators and give a few […]
Binder & Repl.it
I recently discovered two great tools for easily creating interactive coding environments without installing a thing. These tools facilitate sharing of code in multiple languages and are wonderful resources for demonstrating programming concepts when teaching a course. Binder The first tool is called Binder, is open-source and was released in 2017. It is awesome because […]
Creating Projects from Cookiecutter Templates
Ever want to generate a new repo based on a predefined template? Now you can using Cookiecutter! I will show you how to easily spin up a fresh Cookiecutter repo for your latest data science project in Python. Cookiecutter is an awesome command-line tool and Python package that creates projects (aka populates repo folders) based […]
Best 2021 Resources for Learning about AI/ML
For upskilling on AI/ML, I prefer taking a top-down approach i.e. starting with high level concepts then proceeding to more foundational topics (read: delve more into the theory) . I liked taking the breadth-first approach (rather than a depth-first approach) to initially understand AI/ML. Once I had a solid foundation, I easily pivoted to learning […]
PyCon 2020
Hey Folks! I finally got around to watching a bunch of the talks and found several of the talks useful for improving my Python coding skills in general and/or in the context of doing Data Science. Here are some interesting talks from PyCon 2020: Beautiful Python Refactoring video. The talk was simple but powerful in demonstrating […]
Writing Awesome READMEs
This article will explore possible items to include your Git repo README file. We will discuss several items that we can include in a README and when to include them. What is a README? A README file is a text file that explains a project to new user. It helps users quickly understand where to […]
Using Classes in Python
We know that you’ve probably heard of object oriented programming (OOP), but outside of designing games, when is best to use it for data science? We haven’t used OOP much until recently when we refactored a data science project code base. In this article, we give a brief refresher for OOP and discuss our top […]
Staying Up-To-Date on AI/ML
Great Email Newsletters on AI/ML All newsletters are released weekly. Import AI – AI newsletter that summarizes recent news articles and research; I enjoy how honest and succinct this newsletter is; also like that the implications of new algorithms are always discussed by Jack, who is an advocate for improved ML model explicability and data […]
R vs Python
I generally reach for Python when building data science pipelines, however I discovered R before I decided to invest in learning Python. R has saved me lots of time when it came to quickly and easily preparing nice-looking plots for research. It begs the question of where is R better than Python for certain purposes? […]
Export Images to PowerPoint in Python
Data Scientists spend a significant amount of time visualizing data for storytelling or conveying insights to end users of a data product. Often, the ability to succinctly and accurately explain the methods used and insights derived hinges on the medium of communication and time taken to prepare visualizations. In order to limit time spent on […]